Rohit Sharma is creating the most immersive learning experience for working professionals at UpGrad.

We recently checked in with Rohit to get his insight on the data analytics industry including trends to watch out for and must-have skill sets today’s developers should be learning. Here’s what he had to say:

How competitive is the data analytics industry today? What is the demand for these types of professionals?

Let’s talk some numbers:

A widely-quoted McKinsey report states that the United States will face an acute shortage of around 1.5 million data professionals by 2018. In India, which is emerging as the global analytics hub, the shortage of such professionals could go up to as high as 200,000. In India alone, the number of analytics jobs saw a 120 percent rise from June 2015 to June 2016.

So, we clearly have a challenge set out for us. Naturally, because of acute talent shortage, talented professionals are high in demand.

What trends are you following in the data analytics industry today? Why are you interested in them?

There are three key trends that we should watch out for:

Personalization

I think the usage of data to create personalized systems is a key trend being adopted extremely fast, across the board. Most of the internet services are removing the anonymity of online users and moving towards differentiated treatment, for example, words recommendations when you are typing your messages or destinations recommendations when you are using Uber.

End of Moore’s Law

Another interesting trend to watch out for is how companies are getting more and more creative as we reach the end of Moore’s Law. Moore’s Law essentially states that every two years we will be able to fit double the number of transistors that could be fit on a chip, two years ago. Because of this law, we have unleashed the power of storing and processing huge amounts of data, responsible for the entire data revolution. But what will happen next?

IoT

Another trend to watch out for, for the sheer possibilities it brings, is the emergence of smart systems which is made possible by the coming together of cloud, big data and IoT (internet of things).

What skill sets are critical for data engineers today? What do they need to know to stay competitive?

A good data scientist sits at a rare overlap of three areas:

Domain Knowledge: This helps understand and appreciate the nuances of a business problem. For e.g, an e-commerce company that would want to recommend complementary products to its buyers.

Statistical Knowledge: Statistical and mathematical knowledge help to inform data-driven decision making. For instance, one can use market basket analysis to come up with complimentary products for a particular buy.

Technical Knowledge: This helps perform complex analysis at scale; such as creating a recommendation system that shows that a buyer might prefer to also buy a pen while buying a notebook.

Outside of their technical expertise, what other skills should those in data analytics and business intelligence be sure to develop?

Ultimately, data scientists are problem solvers. And every problem has a specific context, content and story behind it. This is where it becomes extremely important to tie all these factors together – into a common narrative. Essentially all data professionals need to be great storytellers.

In this respect, one of the key skills for analysts to sharpen would be, breaking down the complexities of analytics for others working with them, so that they can appreciate the actual insights derived – and work toward a common business goal.

In addition, what is as crucial is getting into a habit of constantly learning (even if it means waking up every morning and reading what’s relevant and current in your domain).

What should these professionals be doing to stay ahead of trends and innovations in the field?

Professionals these days need to continuously upskill themselves and be willing to unlearn and relearn. The world of work and industrial landscape of technology-heavy fields such as data analytics are changing every year and the only way to stay ahead, or even at par with these trends, is to invest in learning, taking up exciting industry-relevant projects, participating in competitions like Kaggle, etc.

How important is mentorship in the data industry? Who can professionals look toward to help further their careers and their skills?

Extremely important. Considering how fast this domain has emerged, academia and universities, in general, have not had the chance to keep up equally fast. Hence, the only way to stay industry-relevant with respect to this domain is to have industry-specific learning.

This can only be done in two ways – through real-life case studies and mentors who are working/senior professionals and hail from the data analytics industry.

In fact, at UpGrad, there is a lot of stress on industry mentorship for aspiring data specialists, in addition to a whole host of case studies and industry-relevant projects.

Where are the best places for data professionals to find mentors?

While it’s important for budding or aspiring data professionals to tap into their networks to find the right mentors, it is admittedly tough to do so. There are two main reasons that can be blamed for this.

First, due to the nascent stage, the industry is at, it is extremely difficult to find someone with the requisite skill sets to be a mentor.

Even if you find someone with considerable experience in the field, not everybody has the time and inclination to be an effective mentor. Hence most people don’t know where to go to be mentored.

That’s where platforms like UpGrad come in, which provide you with a rich, industry-relevant learning experience. Nowhere else are you likely to chance upon such a wide range of industry tie-ups or associations for mentorship from very senior and reputed professionals.

What resources should those in the data analytics industry be using to ensure they’re educated and up-to-date on developments, trends and skills?

There are many, but for starters, here are some good and pretty interesting blogs and resources that would serve aspiring/current data analysts well to keep up with: